Visual object tracking is essential to intelligent robots. Most existing approaches have ignored the online latency that can cause severe performance degradation during real-world processing. Especially for unmanned aerial vehicles (UAVs), where robust tracking is more challenging and onboard computation is limited, the latency issue can be fatal. In this work, we present a simple framework for end-to-end latency-aware tracking, i.e., end-to-end predictive visual tracking (PVT++). Unlike existing solutions that naively append Kalman Filters after trackers, PVT++ can be jointly optimized, so that it takes not only motion information but can also leverage the rich visual knowledge in most pre-trained tracker models for robust prediction. Besides, to bridge the training-evaluation domain gap, we propose a relative motion factor, empowering PVT++ to generalize to the challenging and complex UAV tracking scenes. These careful designs have made the small-capacity lightweight PVT++ a widely effective solution. Additionally, this work presents an extended latency-aware evaluation benchmark for assessing an any-speed tracker in the online setting. Empirical results on a robotic platform from the aerial perspective show that PVT++ can achieve significant performance gain on various trackers and exhibit higher accuracy than prior solutions, largely mitigating the degradation brought by latency. Our code is public at https: //github.com/Jaraxxus-Me/PVT_pp.git.

Vision-based object tracking has boosted extensive autonomous applications for unmanned aerial vehicles (UAVs). However, the dynamic changes in flight maneuver and viewpoint encountered in UAV tracking pose significant difficulties, e.g., aspect ratio change, and scale variation. The conventional cross-correlation operation, while commonly used, has limitations in effectively capturing perceptual similarity and incorporates extraneous background information. To mitigate these limitations, this work presents a novel saliency-guided dynamic vision Transformer (SGDViT) for UAV tracking. The proposed method designs a new task-specific object saliency mining network to refine the cross-correlation operation and effectively discriminate foreground and background information. Additionally, a saliency adaptation embedding operation dynamically generates tokens based on initial saliency, thereby reducing the computational complexity of the Transformer architecture. Finally, a lightweight saliency filtering Transformer further refines saliency information and increases the focus on appearance information. The efficacy and robustness of the proposed approach have been thoroughly assessed through experiments on three widely-used UAV tracking benchmarks and real-world scenarios, with results demonstrating its superiority. The source code and demo videos are available at https://github.com/vision4robotics/SGDViT.

Consumer robots can accompany children growing up, improving their abilities while playing and entertaining. This paper presents an open-source, practical, low-cost robotic Chinese chess player. The proposed system includes an elaborate mechanical structure, a simple kinematic solution, a novel robot operating system, real-time and accurate chess recognition. Regarding its mechanical design, it combines a magnetism structure and mechanical cam drive, while the overall system has just three servo motors. At the same time, its control strategy is simple and effective. Furthermore, a lightweight robot message communication mechanism, entitled TinyROS, is developed for computing resource-limited embedded chips. Concerning the recognition process, our CNNbased object detector determines chess and achieves accurate identification. As a result, our robotic Chinese chess player is exquisite and easy for large-scale promotion while improving users' chess skills. Aiming to facilitate future consumer robot research and popularize customer robots, the model's mechanical and software design and the TinyROS protocol are open-sourced at https://github.com/Star-Robot/chinese-chess-robot.

In many industrial applications of unmanned aerial manipulator (UAM), visual approaching the object is crucial to subsequent manipulating. In comparison with the widely-studied manipulating, the key to efficient vision-based UAM approaching, i.e., UAM object tracking, is still limited. Since traditional model-based UAM tracking is costly and cannot track arbitrary objects, an intuitive solution is to introduce state-of-the-art model-free Siamese trackers from the visual tracking field. Although Siamese tracking is most suitable for the onboard embedded processors, severe object scale variation in UAM tracking brings formidable challenges. To address these problems, this work proposes a novel model-free scale-aware Siamese tracker (SiamSA). Specifically, a scale attention network is proposed to emphasize scale awareness in feature processing. A scale-aware anchor proposal network is designed to achieve anchor proposing. Besides, two novel UAM tracking benchmarks are first recorded. Comprehensive experiments on benchmarks validate the effectiveness of SiamSA. Furthermore, real-world tests also confirm practicality for industrial UAM approaching tasks with high efficiency and robustness.

Unmanned aerial vehicle (UAV) has facilitated a wide range of real-world applications and attracted extensive research in the mobile computing field. Specially, developing real-time robust visual onboard trackers for all-day aerial maneuver can remarkably broaden the scope of intelligent deployment of UAV. However, prior tracking methods have merely focused on robust tracking in the well-illuminated scenes, while ignoring trackers’ capabilities to be deployed in the dark. In darkness, the conditions can be more complex and harsh, easily posing inferior robust tracking or even tracking failure. To this end, this work proposes a novel discriminative correlation filter-based tracker with illumination adaptive and anti-dark capability, namely ADTrack. ADTrack firstly exploits image illuminance information to enable adaptability of the model to the given light condition. Then, by virtue of an efficient enhancer, ADTrack carries out image pretreatment where a target aware mask is generated. Benefiting from the mask, ADTrack aims to solve a novel dual regression problem where dual filters are online trained with mutual constraint. Besides, this work also constructs a UAV nighttime tracking benchmark UAVDark135. Exhaustive experiments on authoritative benchmarks and onboard tests are implemented to validate the superiority and robustness of ADTrack in all-day conditions.

Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications. This work instead develops a novel unsupervised domain adaptation framework for nighttime aerial tracking (named UDAT). Specifically, a unique object discovery approach is provided to generate training patches from raw nighttime tracking videos. To tackle the domain discrepancy, we employ a Transformer-based bridging layer post to the feature extractor to align image features from both domains. With a Transformer day/night feature discriminator, the day-time tracking model is adversarially trained to track at night. Moreover, we construct a pioneering benchmark namely NAT2021 for unsupervised domain adaptive night-time tracking, which comprises a test set of 180 manually annotated tracking sequences and a train set of over 276k unlabelled nighttime tracking frames. Exhaustive experiments demonstrate the robustness and domain adaptability of the proposed framework in nighttime aerial tracking. The code and benchmark are available at https://github.com/vision4robotics/UDAT.

Visual tracking is adopted to extensive unmanned aerial vehicle (UAV)-related applications, which leads to a highly demanding requirement on the robustness of UAV trackers. However, adding imperceptible perturbations can easily fool the tracker and cause tracking failures. This risk is often overlooked and rarely researched at present. Therefore, to help increase awareness of the potential risk and the robustness of UAV tracking, this work proposes a novel adaptive adversarial attack approach, i.e., Ad2Attack, against UAV object tracking. Specifically, adversarial examples are generated online during the resampling of the search patch image, which leads trackers to lose the target in the following frames. Ad2Attack is composed of a direct downsampling module and a super-resolution upsampling module with adaptive stages. A novel optimization function is proposed for balancing the imperceptibility and efficiency of the attack. Comprehensive experiments on several well-known benchmarks and real-world conditions show the effectiveness of our attack method, which dramatically reduces the performance of the most advanced Siamese trackers.

Although the manipulating of the unmanned aerial manipulator (UAM) has been widely studied, vision-based UAM approaching, which is crucial to the subsequent manipulating, generally lacks effective design. The key to the visual UAM approaching lies in object tracking, while current UAM tracking typically relies on costly model-based methods. Besides, UAM approaching often confronts more severe object scale variation issues, which makes it inappro-priate to directly employ state-of-the-art model-free Siamese-based methods from the object tracking field. To address the above problems, this work proposes a novel Siamese network with pairwise scale-channel attention (SiamSA) for vision-based UAM approaching. Specifically, SiamSA consists of a pairwise scale-channel attention network (PSAN) and a scale-aware anchor proposal network (SA-APN). PSAN acquires valuable scale information for feature processing, while SA-APN mainly attaches scale awareness to anchor proposing. Moreover, a new tracking benchmark for UAM approaching, namely UAMT100, is recorded with 35K frames on a flying UAM platform for evaluation. Exhaustive experiments on the benchmarks and real-world tests validate the efficiency and practicality of SiamSA with a promising speed. Both the code and UAMT100 benchmark are now available at https://github.com/vision4robotics/SiamSA.

Low-light environments have posed a formidable challenge for robust unmanned aerial vehicle (UAV) tracking even with state-of-the-art (SOTA) trackers since the poten-tial image features are hard to extract under adverse light conditions. Besides, due to the low visibility, accurate online selection of the object also becomes extremely difficult for human monitors to initialize UAV tracking in ground con-trol stations. To solve these problems, this work proposes a novel enhancer, i.e., HighlightNet, to light up potential objects for both human operators and UAV trackers. By employing Transformer, HighlightNet can adjust enhancement parameters according to global features and is thus adaptive for the illumination variation. Pixel-level range mask is introduced to make HighlightNet more focused on the enhancement of the tracking object and regions without light sources. Furthermore, a soft truncation mechanism is built to prevent background noise from being mistaken for crucial features. Evaluations on image enhancement benchmarks demonstrate HighlightNet has advantages in facilitating human perception. Experiments on the public UAVDark135 benchmark show that HightlightNet is more suitable for UAV tracking tasks than other state-of-the-art (SOTA) low-light enhancers. In addition, real-world tests on a typical UAV platform verify HightlightNet's practicability and efficiency in nighttime aerial tracking-related applications. The code and demo videos are available at https://github.com/vision4robotics/HighlightNet.

Transformer-based visual object tracking has been utilized extensively. However, the Transformer structure is lack of enough inductive bias. In addition, only focusing on encoding the global feature does harm to modeling local details, which restricts the capability of tracking in aerial robots. Specifically, with local-modeling to global-search mechanism, the proposed tracker replaces the global encoder by a novel local-recognition encoder. In the employed encoder, a local-recognition attention and a local element correction network are carefully designed for reducing the global redundant information interference and increasing local inductive bias. Meanwhile, the latter can model local object details precisely under aerial view through detail-inquiry net. The proposed method achieves competitive accuracy and robustness in several authoritative aerial benchmarks with 316 sequences in total. The proposed tracker's practicability and efficiency have been validated by the real-world tests. The source code is available at https://github.com/vision4robotics/LPAT.

Object feature pollution is one of the burning issues in vision-based UAV tracking, commonly caused by occlusion, fast motion, and illumination variation. Due to the contaminated information in the polluted object features, most trackers fail to precisely estimate the object location and scale. To address the above disturbing issue, this work proposes a novel end-to-end feature decontaminated network for efficient and effective UAV tracking, i.e., FDNT. FDNT mainly includes two modules: a decontaminated downsampling network and a decontaminated upsampling network. The former reduces the interference information of the feature pollution and enhanced the expression of the object location information with two asymmetric convolution branches. The latter restores the object scale information with the super-resolution technology-based low-to-high encoder, achieving a further decontamination effect. Moreover, a novel pooling distance loss is carefully developed to assist the decontaminated downsampling network in concentrating on the critical regions with the object information. Exhaustive experiments on three well-known benchmarks validate the effectiveness of FDNT, especially on the sequences with feature pollution. In addition, real-world tests show the efficiency of FDNT with 31.4 frames per second. The code and demo videos are available at https://github.com/vision4robotics/FDNT.

Object tracking approaches based on the Siamese network have demonstrated their huge potential in the remote sensing field recently. Nevertheless, due to the limited computing resource of aerial platforms and special challenges in aerial tracking, most existing Siamese-based methods can hardly meet the real-time and state-of-the-art performance simultaneously. Consequently, a novel Siamese-based method is proposed in this work for onboard real-time aerial tracking, i.e., SiamAPN. The proposed method is a no-prior two-stage method, i.e., Stage-1 for proposing adaptive anchors to enhance the ability of object perception and Stage-2 for fine-tuning the proposed anchors to obtain accurate results. Distinct from the traditional predefined anchors, the proposed anchors can adapt automatically to the tracking object. Besides, the internal information of adaptive anchors is utilized to feedback SiamAPN for enhancing the object perception. Attributing to the feature fusion network, different semantic information is integrated, enriching the information flow that is significant for robust aerial tracking. In the end, the regression and multiclassification operation refine the proposed anchors meticulously. Comprehensive evaluations on three well-known aerial tracking benchmarks have proven the superior performance of the presented approach. Moreover, to verify the practicability of the proposed method, SiamAPN is implemented onboard a typical embedded aerial tracking platform to conduct the real-world evaluations on specific aerial tracking scenarios, e.g., fast motion, long-term tracking, and low resolution. The results have demonstrated the efficiency and accuracy of the proposed approach, with a processing speed of over 30 frames/s. In addition, the image sequences in the real-world evaluations are collected and annotated as a new aerial tracking benchmark, i.e., UAVTrack112.

Vision-based aerial tracking has proven enormous potential in the field of remote sensing recently. However, challenges such as occlusion, fast motion, and illumination variation remain crucial issues for realistic aerial tracking applications. These challenges, frequently occurring from the aerial perspectives, can easily cause object feature pollution. With the contaminated object features, the credibility of trackers is prone to be substantially degraded. To address this issue, this work proposes a novel end-to-end decontaminated network, i.e., DeconNet, to alleviate object feature pollution efficiently and effectively. DeconNet mainly consists of downsampling and upsampling phases. Specifically, the decontaminated downsampling network first decreases the polluted object information with two convolution branches, enhancing the object location information. Subsequently, the decontaminated upsampling network applies the super-resolution technology to restore the object scale and shape information, with the low-to-high (LTH) encoder for further decontamination. In addition, the pooling distance (PD) loss function is carefully designed to improve the decontamination effect of the decontaminated downsampling network. Comprehensive evaluations on four well-known aerial tracking benchmarks validate the effectiveness of DeconNet. Especially, the proposed tracker has superior performance on the sequences with feature pollution. Besides, real-world tests on an aerial platform have proven the efficiency of DeconNet with 30.6 fps.

Most existing Siamese-based tracking methods execute the classification and regression of the target object based on the similarity maps. However, they either employ a single map from the last convolutional layer which degrades the localization accuracy in complex scenarios or separately use multiple maps for decision making, introducing intractable computations for aerial mobile platforms. Thus, in this work, we propose an efficient and effective hierarchical feature transformer (HiFT) for aerial tracking. Hierarchical similarity maps generated by multi-level convolutional layers are fed into the feature transformer to achieve the interactive fusion of spatial (shallow layers) and semantics cues (deep layers). Consequently, not only the global contextual information can be raised, facilitating the target search, but also our end-to-end architecture with the transformer can efficiently learn the interdependencies among multi-level features, thereby discovering a tracking-tailored feature space with strong discriminability. Comprehensive evaluations on four aerial benchmarks have proven the effectiveness of HiFT. Real-world tests on the aerial platform have strongly validated its practicability with a real-time speed. Our code is available at https://github.com/vision4robotics/HiFT.

In the domain of visual tracking, most deep learning-based trackers highlight the accuracy but casting aside efficiency. Therefore, their real-world deployment on mobile platforms like the unmanned aerial vehicle (UAV) is impeded. In this work, a novel two-stage Siamese network-based method is proposed for aerial tracking, i.e., stage-1 for high-quality anchor proposal generation, stage-2 for refining the anchor proposal. Different from anchor-based methods with numerous pre-defined fixed-sized anchors, our no-prior method can 1) increase the robustness and generalization to different objects with various sizes, especially to small, occluded, and fast-moving objects, under complex scenarios in light of the adaptive anchor generation, 2) make calculation feasible due to the substantial decrease of anchor numbers. In addition, compared to anchor-free methods, our framework has better performance owing to refinement at stage-2. Comprehensive experiments on three benchmarks have proven the superior performance of our approach, with a speed of ∼200 frames/s.

Prior correlation filter (CF)-based tracking methods for unmanned aerial vehicles (UAVs) have virtually focused on tracking in the daytime. However, when the night falls, the trackers will encounter more harsh scenes, which can easily lead to tracking failure. In this regard, this work proposes a novel tracker with anti-dark function (ADTrack). The proposed method integrates an efficient and effective low-light image enhancer into a CF-based tracker. Besides, a target-aware mask is simultaneously generated by virtue of image illumination variation. The target-aware mask can be applied to jointly train a target-focused filter that assists the context filter for robust tracking. Specifically, ADTrack adopts dual regression, where the context filter and the target-focused filter restrict each other for dual filter learning. Exhaustive experiments are conducted on typical dark sceneries benchmark, consisting of 37 typical night sequences from authoritative benchmarks, i.e., UAVDark, and our newly constructed benchmark UAVDark70. The results have shown that ADTrack favorably outperforms other state-of-the-art trackers and achieves a real-time speed of 34 frames/s on a single CPU, greatly extending robust UAV tracking to night scenes.

Unmanned aerial vehicle (UAV) based visual tracking has been confronted with numerous challenges, e.g., object motion and occlusion. These challenges generally introduce unexpected mutations of target appearance and result in tracking failure. However, prevalent discriminative correlation filter (DCF) based trackers are insensitive to target mutations due to a predefined label, which concentrates on merely the centre of the training region. Meanwhile, appearance mutations caused by occlusion or similar objects usually lead to the inevitable learning of wrong information. To cope with appearance mutations, this paper proposes a novel DCF-based method to enhance the sensitivity and resistance to mutations with an adaptive hybrid label, i.e., MSCF. The ideal label is optimized jointly with the correlation filter and remains temporal consistency. Besides, a novel measurement of mutations called mutation threat factor (MTF) is applied to correct the label dynamically. Considerable experiments are conducted on widely used UAV benchmarks. The results indicate that the performance of MSCF tracker surpasses other 26 state-of-the- art DCF-based and deep-based trackers. With a real-time speed of ~38 frames/s, the proposed approach is sufficient for UAV tracking commissions.

Recently, the Siamese-based method has stood out from multitudinous tracking methods owing to its state-of-the-art (SOTA) performance. Nevertheless, due to various special challenges in UAV tracking, e.g., severe occlusion and fast motion, most existing Siamese-based trackers hardly combine superior performance with high efficiency. To this concern, in this paper, a novel attentional Siamese tracker (SiamAPN++) is proposed for real-time UAV tracking. By virtue of the attention mechanism, we conduct a special attentional aggregation network (AAN) consisting of self-AAN and cross-AAN for raising the representation ability of features eventually. The former AAN aggregates and models the self-semantic interdependencies of the single feature map via spatial and channel dimensions. The latter aims to aggregate the cross-interdependencies of two different semantic features including the location information of anchors. In addition, the anchor proposal network based on dual features is proposed to raise its robustness of tracking objects with various scales. Experiments on two well-known authoritative benchmarks are conducted, where SiamAPN++ outperforms its baseline SiamAPN and other SOTA trackers. Besides, real-world tests onboard a typical embedded platform demonstrate that SiamAPN++ achieves promising tracking results with real-time speed.

Recent years have witnessed the fast evolution and promising performance of the convolutional neural network (CNN)-based trackers, which aim at imitating biological visual systems. However, current CNN-based trackers can hardly generalize well to low-light scenes that are commonly lacked in the existing training set. In indistinguishable night scenarios frequently encountered in unmanned aerial vehicle (UAV) tracking-based applications, the robustness of the state-of-the-art (SOTA) trackers drops significantly. To facilitate aerial tracking in the dark through a general fashion, this work proposes a low-light image enhancer namely DarkLighter, which dedicates to alleviate the impact of poor illumination and noise iteratively. A lightweight map estimation network, i.e., ME-Net, is trained to efficiently estimate illumination maps and noise maps jointly. Experiments are conducted with several SOTA trackers on numerous UAV dark tracking scenes. Exhaustive evaluations demonstrate the reliability and universality of DarkLighter, with high efficiency. Moreover, DarkLighter has further been implemented on a typical UAV system. Real-world tests at night scenes have verified its practicability and dependability.

Virtual try-on technology enables users to try various fashion items using augmented reality and provides a convenient online shopping experience. However, most previous works focus on the virtual try-on for clothes while neglecting that for shoes, which is also a promising task. To this concern, this work proposes a real-time augmented reality virtual shoe try-on system for smartphones, namely ARShoe. Specifically, ARShoe adopts a novel multi-branch network to realize pose estimation and segmentation simultaneously. A solution to generate realistic 3D shoe model occlusion during the try-on process is presented. To achieve a smooth and stable try-on effect, this work further develop a novel stabilization method. Moreover, for training and evaluation, we construct the very first large-scale foot benchmark with multiple virtual shoe try-on task-related labels annotated. Exhaustive experiments on our newly constructed benchmark demonstrate the satisfying performance of ARShoe. Practical tests on common smartphones validate the real-time performance and stabilization of the proposed approach.

Aerial object tracking approaches based on discriminative correlation filter (DCF) have attracted wide attention in the tracking community due to their impressive progress recently. Many studies introduce temporal regularization into the DCF-based framework to achieve a more robust appearance model and further enhance the tracking performance. However, existing temporal regularization approaches usually utilize the information of two consecutive frames, which are not robust enough due to limited information. Although some methods attempt to incorporate abundant training samples and generally improve the tracking performance, these improvements are at the expense of significantly increased computing consumption. Besides, most existing methods introduce historical information directly without denoising, which means that background noises are also introduced into the filter training and may degrade the tracking accuracy. To tackle the drawbacks mentioned earlier, this work proposes a novel aerial object tracking approach to exploit disruptor-aware interval-based response inconsistency, i.e., IBRI tracker. The proposed method is able to incorporate historical interval information by utilizing responses in the filter training process, thereby obtaining a robust tracking performance while maintaining the real-time speed. Moreover, to reduce the disruptions caused by similar object, partial occlusion, and other challenging scenes, a novel disruptor-aware scheme based on response bucketing is introduced to detect the disruptor and enforce a spatial penalty for the disruptive area around the tracked object. Exhausted experiments on multiple well-known challenging aerial tracking benchmarks demonstrate the accuracy and robustness of the proposed IBRI tracker against other 35 state-of-the-art trackers. With a real-time speed of ~32 frames/s on a single CPU, the proposed approach can be applied for typical aerial platforms to achieve aerial visual object tracking efficiently.